{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**数据可视化**指的是通过可视化表示来探索数据，它与**数据挖掘**紧密相关，漂亮地呈现数据关乎的并非仅仅是漂亮的图片。以引人注目的简洁方式呈现数据，让观看者能够明白其含义，发现数据集中原本未意识到的规律和意义。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最流行的工具之一是**matplotlib**，它是一个数学绘图库，我们将使用它来制作简单的图表，如折线图和散点图。我们还将使用**Pygal包**，它专注于生成适合在数字设备上显示的图表。通过使用Pygal，可在用户与图表交互时突出元素以及调整其大小，还可轻松地调整整个图表的尺寸，使其适合在微型智能手表或巨型显示器上显示。我们将使用Pygal以各种方式探索掷骰子的结果。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 15.1 安装matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "测试一下是否安装成功："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "没有报错，安装成功！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 15.2 绘制折线图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面来使用matplotlib绘制一个简单的折线图，再对其进行定制，以实现信息更丰富的数据可视化。我们将使用平方数序列1、4、9、16和25来绘制这个图表。<br></br>\n",
    "只需向matplotlib提供如下数字，matplotlib就能完成其他的工作："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "squares = [1, 4, 9, 16, 25]\n",
    "plt.plot(squares)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15.2.1 修改线条文字和线条粗细"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 设置线条粗细为5，并查看效果\n",
    "plt.plot(squares, linewidth=5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果指定linewidth的话，默认为1。所以当我们将线条宽度指定为5时，折线明显的变粗了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "但是此时，只有图的话，你可能不会很好理解是什么意思，因此添加标签文字。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(squares, linewidth=5)\n",
    "\n",
    "# 设置图标标题，并给坐标轴添加标签\n",
    "plt.title(\"$y=x^2$\")  # letex语法\n",
    "plt.xlabel(\"Value\", fontsize=14)\n",
    "plt.ylabel(\"Square Of Value\", fontsize=14)\n",
    "# 设置刻度标记大小\n",
    "plt.tick_params(axis='both', labelsize=14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这样看起来是不是舒服多了呢？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15.2.2 校正图片"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "虽然阅读起来舒服多了，但是图片的数据却是不正确的。e.g. 4的平方怎么是25呢？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "要解决这个问题，首先我们要明白为什么计算机会画出这种二维图，明明我们只给了一个一维向量啊。在plot函数定义中，如果没有x轴的向量，是默认[0, 1, 2, 3, 4, 5, ..., n]。我们就不难理解为什么会出现4的平方是25了，因为[4, 25]是在plot中对应的，它只接收数据，并不会验证数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aLSnYwVlP5rF2W/gzMM0bZvL8hUPod1CzJFRWe8UcDmaWAQwmWP0tq+yYu/8jznWJiACwaN12xjyVR8H2vWFjLRtl8cJFQzi0Q9MkVFa7xToJrjfwFsEjrQaUhD63CNgLKBxEJO7m5W/j7Kfy2LizMGwsu3E9xo8dwiFtmyShstov1vsKDwAzgGbALqAPkAt8CfysOgoTkbpt7uqtnPnk1IjB0LZpPV755VAFQzWK9bLSIOA4d99pZqVAhrvPNLPrgYeAw6qtQhGpc75cuYVzn85j257isLEOzeozfuxQumQ3SkJldUesZw5GcMYAUADs62C1imAtaRGRuJi+fBNnPxU5GA5q0YBXfnmkgiEBYj1zmAscDiwFvgBuMLMSYCywuJpqE5E6ZurSjVw4bhq7CkvCxrq0asj4sUPp0LxBEiqre2INhz8B+6L6ZuA/wIfABmB0NdQlInXMe9+s44qXZrKnqDRsrHvrRowfO5S2TesnobK6KWo4mNmnwJPAq+6+G8DdlwJ9zKwlsNndPdoxRESiKSop5b53F/D4J0sjjvdq24QXLh5C6yb1ElxZ3VbZPYfVwONAvpk9Zma5+wbcfZOCQUQORP7W3Zz5xNQKg+HQ9k156ZKhCoYkiBoO7n460AG4DTgSyDOz2WZ2hZlpnrqI7LdPFhZw2t8+ZfqKzRHHDzuoGePHDqFlo6yI41K9Kn1aKXSG8KC7Hw4MBaYAfwDWmNlLZnZCdRcpIrVHSalz/6QFnPfsF2yKMIcB4MTebXjh4iE0b6hgSJYq9VZy92nANDO7GjgTuI/ghrS6sopIpdZv38NvXvqSz5dujDienmZcP6IXY4/pRlpa+YUnJZGq3HjPzA4BLgTOBZoCb8e7KBGpfT5fspErX54VsUcSBLOeHx4zUC23U0RMk+DMrKGZXRB6emke8FPgb0And/9RrG9mZsea2b/NbLWZuZmdX258XGh72Y+pVfj7iEiKKS11HvlwMWc9NbXCYDimZzb/ufIYBUMKqexR1qMJzhJ+Edr3deD37v7xfr5fY4IJdf+g4mZ97wPnlHkd+aKkiKS8TTsLufqVL/l4YUHEcTO4+qRDuHx4D9J1GSmlVHZZ6b8EzfVuBF50960H8mbu/g7wDgRnCRXsttfd1x7I+4hI8s1YsYlfj59F/tbwNRgAshtn8eAZAzi6h5b0TEWVhcMR7j4rIZV8Z5iZrQe2AB8TnKmsT3ANIrKf3J2nP13G3RPmU1waeSrUkK4teejMAbTRjOeUFTUckhAME4E3gGVAF+B/gA/M7Ah3D7tYaWaXAJcAdOrUKYFlikgkW3cVce1rs3nvm3UV7nP58O5cfdIhZKRrJeJUllLLhLr7y2VezjGzGcAK4DSC0Ci//xPAEwC5ubmarS2SRF+t2sLl42eyclP4Gs8QLOf519P7M7xXmwRXJvsjpcKhPHdfY2argJ7JrkVEInN3np+6gv95ex6FJeFN8wAGdGrOw2MG0lEdVWuMCsPBzI4Fprh7eFP1BDGzbIK1I/KTVYOIVGz7niJufGMO//mq4n+iFw/ryvUje5OVoctINUm0M4cPgfbAejNbCgxy98jTGmNkZo35bnGgNKCTmfUHNoU+bid4XDaf4J7DXcB64J8H8r4iEn/frNnG5eNnsmzDzojjTepn8OefH87InHYJrkziIVo4bAa6Evxw7kLsq8ZFk0sQOvvcEfp4DvgV0I9g5nVzgoD4EBjt7tvj8N4iEgfuzqvTV3Lrv75mb3Hky0g5HZvy6Jgj6NSqYYKrk3iJFg6vAx+bWT7gwPTQ6m9h3L1bLG/m7h8RLDlakRGxHEdEkmNXYTE3vzmXN2aurnCfc4Z25ven9aF+plqu1WTRwuFS4N8EN4PvB54F9Bu8SB21eP12LntxJgvX7Yg43igrnbt+dhg/PLxDgiuT6lBhOIQW8vkPgJkdDvxFl3dE6qY3Z63md/+cE3FtZ4De7ZrwyFkD6d66cYIrk+oS06Os7n6BmTUrsxLcYnffUn1liUgq2FNUwh1vfcNLX3xb4T6jcw/ijh/m0CBLl5Fqk0rDwcw6AY8Ao/jufoGb2TvAFe6+ohrrE5EkWb5hJ5e9OJNv8rdFHK+fmcYff5TDL3IPTnBlkgiVdWXtCEwFSoFbgW9CQ32By4ApZjbI3ddUa5UiklDvzMnn+te+YsfeyNOcurVuxN/POoJe7ZokuDJJlMrOHG4j6HN0kruXnRP/ppn9FZgU2ueX1VSfiCRQYXEpd74zj3FTlle4z4/6d+DOn/SjUb2UbrAgB6iy/7unAmeVCwYA3H2Xmd0MvFAtlYlIQq3ctItfvzSL2Su3RBzPykjjth8cypjBnTDT2gu1XWXh0BpYEmV8cWgfEanBJs9bxzWvzmbr7qKI451aNuTRswaS07FZgiuTZKksHNYTtLtYVcF4z9A+IlIDFZWUct+kBTz+8dIK9xnZtx33/uIwmtbPTGBlkmyVhcME4H/M7MTy6ymYWX3gj4RWdhORmmXt1j1c8dJMpi3fHHE8I8343al9uODoLrqMVAdVFg63A9OBxWb2MDA/tP1QgqeVMoDTq606EakWnyws4KpXvmTTzshLtHds3oCHxgxgYKcWCa5MUkVlK8GtMbOjgEeBOykzzwF4F/i1u1fcZEVEUkpJqfPg5EU89MEivILlsU7o3Ya//OJwWjTKSmxxklIqfRbN3ZcDp5pZC75bdGexu2+qzsJEJL4Ktu/lNy/PYsqSyJ3309OM60b04pJjupGWpstIdV3MDyq7+2bgi2qsRUSqyedLNnLly7Mo2B62FDsAbZrU46EzBzCkW6sEVyapSrNYRGqx0lLn7x8v4S+TFlBawWWkYT2yeeCM/mQ3rpfY4iSlKRxEaqnNOwu5+tUv+WhBQcRxM/jNiT254oSepOsykpSjcBCphWas2MwV42eyZuueiOOtGmXx4BkDGNYzO8GVSU2hcBCpRdydpz9dxt0T5lNcwXWkwV1b8tCZA2jbtH6Cq5OaJOZwMLN+BA32ugMXunu+mf0YWOHus6qpPhGJ0dbdRVz3v7OZ9M26Cve57PjuXHPyIWSkx2NJeKnNYgoHMzuFYMnQCcAJQIPQUHfgfODH1VCbiMRozqqtXDZ+Bis3hfXIBKB5w0z+Oro/w3u3SXBlUlPFeubwR+Aad3/UzMouFfoR8Nu4VyUiMXF3Xpi6gj++PY/CktKI+/Q/uDmPnDWQjs0bRBwXiSTWcMghcg+lTUDL+JUjIrHasbeYG1//ire/yq9wnwuP7sqNo3qTlaHLSFI1sYbDJqAjsLzc9oFU3LFVRKrJvPxtXP7iTJZu2BlxvEm9DP78i8MYmdM+wZVJbRFrOIwH/mxmown6KmWY2XHAfcCz1VWciIR7dfpKbnlzLnuLI19G6tuhKY+eNZDOrRoluDKpTWINh5uBccAKguZ734T+HA/8qVoqE5Hv2VVYzC1vfs3rMys+WT97aCduPu1Q6memJ7AyqY0qDQczSyNouPdL4BaCS0lpwCx3X1S95YkIwOL127nsxZksXLcj4njDrHTu+mk/ftS/Y4Irk9oqljMHB74EDnX3xUDFS0aJSNz968vV3PTGHHYVlkQc79W2CY+cNZAebRonuDKpzWJp2e1mtoBgrejF1V+SiADsKSrhD29/w/i8byvc5+dHHMQff5RDgyxdRpL4ivWew/XAfWZ2OTDbvaJlQkQkHpZv2MllL87km/xtEcfrZ6bxhx/lMDr34ARXJnVFrOHwKlAfmAEUm9n3msK7e9N4FyZSV02Yk8/1r33F9r3FEce7tW7Eo2cNpHc7/bOT6hNrOPw6Hm9mZscC1wJHAB2AC9x9XJlxA24DLgFaAHnA5e7+dTzeXySVFRaXcteEeTz72fIK9/nB4R2466f9aFxPPTOlesX0Hebuz8Xp/RoDc4F/hD7Ku56gHcf5wALgVuA9M+vl7tsj7C9SK6zavIvLx89i9sotEcez0tO49QeHctaQTgS/Q4lUryr/+mFm7YDvrTzu7hXfMfv+fu8QasNhZuPKHdeAq4C73f310LbzgPXAGODxqtYqUhNMnreOa16dzdbdRRHHO7VsyKNnDSSnY7MEVyZ1WaxdWZsBfwNGUy4YQuLxqERXoB0wad8Gd99tZp8AR6FwkFqmuKSU+yYt5LGPl1S4z4i+bbn354fTrEFmAisTif3M4T7gcILW3G8AFxL0WvoN8evK2i70Z/lm9OtC7xXGzC4huD9Bp06d4lSGSPVbu3UPV740iy+Wb4o4npFm3HRqHy48uosuI0lSxBoOo4Az3f2/ZlYCzHD3V8wsn2Dm9GvVVmEU7v4E8ARAbm6uHq+VGuG/iwq46uUv2bizMOJ4h2b1efisgQzs1CLBlYl8J9ZwaE7QVwlgK9CKYELc58BTcaplbejPtkDZexhty4yJ1Fglpc6Dkxfx0AeLqGim0PBerbl/dH9aNIp09VYkcWJt8r4E6Bb673nAGaEbyD8laOcdD8sIQuDkfRvMrD5wDDAlTu8hkhQF2/dy7jN5/G1y5GBIM7h+ZC+ePm+QgkFSQqxnDuOAwwhWfrsbeJtg7kMawX2HmJhZY6BH6GUa0MnM+gOb3P1bM3sA+J2ZzQcWEnSD3UHQ/VWkRspbupErXprF+u17I463aVKPv505gKHdWiW4MpGKxTrP4a9l/vsDM+sN5AKL3H1OFd4vF/iwzOs7Qh/PEcxtuJdgfepH+G4S3Cma4yA1UWmp89gnS7jv3QWUVnAZ6egerXjg9AG0blIvscWJVGK/plmG5jXENLeh3Od9RLAOREXjDtwe+hCpkdydTxZt4O4J85lXQW8kM7jyhJ5ceWJP0tP0NJKknljnOVwTbdzd749POSI125xVW7lrwjymLNlY4T6tGmXxwBn9OaZn6wRWJlI1sZ45XFHudSbQHthNMINZ4SB12oqNO7lv0kLemr0m6n6DurTgoTMH0q5Z/QRVJrJ/Yr3n0LX8NjNrS7B+9JPxLkqkptiwYy8Pf7CYF/NWUFQSfarNpcd159pTDiEjPdaHBEWSZ79bO7r7OjP7PUE773/GrySR1LdzbzFPf7qMxz9ews4KVmjbp//BzfndqX0Y3LVlgqoTOXAH2vc3jWCSmkidUFRSyivTVvLA+4vYsCPyo6n7dM1uxPUjejEyp51aYEiNE+sN6Z+W30Rwz+Fy4L/xLkok1bg7E+eu5c/vLmDphp1R981uXI+rTurJ6YMOJlOXkKSGivXMoXzvJAcKgA+IX+M9kZSUt3Qjd02Yz5cVrLWwT6OsdH55XHcuGtaVRlqMR2q4WG9I69cfqXMWrN3OPRPn88H89VH3y0gzzh7amV+f0IPsxprMJrWDfr0RKWfNlt3c/95CXp+5qsIGefv8v8Pac92IXnRu1SgxxYkkSKz3HG6N9YDu/of9L0ckebbuKuLRjxbz7JTlFBaXRt33qO6tuHFUbw47qHliihNJsFjPHH4BdAYaAvtm+XQAdvFdK28I7kUoHKRG2VNUwnNTlvPIh4vZtqc46r592jflxlG9ObZntp5Aklot1nC4HzgXOG/fetFm1olgEtyL7v5MNdUnUm1KSp1/zlrN/ZMWsGbrnqj7dmzegGtHHMKPDu9ImnohSR0QazjcCvx4XzBA0HzPzH4L/AtQOEiN4e58tKCAeybOZ/7a6A1/mzfM5NfDe3D20M7Uz4zHUukiNUOs4dCWoJV2efWB7PiVI1K9vly5hbvemUfesuhrVNXLSOPCYV259LjuNGuQmaDqRFJHrOHwHvCkmY0FphHcWxgMPB4aE0lpyzbs5M/vzuedOdFXnE0z+MURB3P1yYeoOZ7UabGGw8UEC/JMAfY1kkkD3gXGVkNdInFRsH0vD05eyMtfrKS4ohV3Qk7q05YbRvaiZ9smCapOJHXFOgmuADjVzHoCfUKb57v7wmqrTOQA7NhbzBOfLOWp/y5lVyWN8QZ2as5Np/ZhUBc1xhPZp0qT4Nx9EbDIzDII7jeIpJTC4lJenvYtf5u8iA07CqPu2611I24Y2ZtTDm2rx1JFyokaDmZ2ItDK3V8ts+1GgmU8M8zsfeAMd99SnUWKVMbd+c+cfP787gJWbNwVdd82Tepx1UmHMDr3IK2tIFKBys4cbgQm7HthZoOBO4GngXnAdcDvQ3+KJMWUJcF6zV+t2hp1v8b1Mrj0uG5cOKwrDbPUOUYkmsr+hfQjCIh9fgFMcfexAGa2EvgfFA6SBPPyt3H3hPl8vLAg6n6Z6UFjvCtO6EnLRlkJqk6kZqssHJoTrBG9z9HAO2VeTwM6xrkmkahWbd7F/ZMW8s8vV1faGO9H/Ttw7Sm9OLhlw8QUJ1JLVBYO+UB3YKWZ1QMGALeUGW8CRF8OSyRONu8s5NGPFvPclBUUlkRvjHdMz2xuGNmbnI7NElSdSO1SWThMAO4N3YT+IbCT76/8dhiwuJpqEwGCxnjPfracRz9azPZKGuP17RA0xjumZ+sEVSdSO1UWDrcCbwDvAzsIGu+VfT7wQjRDWqpJSanz+oxV3P/eQtZui94Y76AWDbhuRC9+cFgHNcYTiYOo4eDuG4BjzawZsMPdy88m+gVBaIjEjbszed567pk4n0Xro397tWiYyRUn9OSsoZ2ol6HGeCLxEusM6YjPCLp79O5lIlU0Y8Vm7pkwny+WR//Wqp+ZxsXDunHJcd1oWl+N8UTiTQ97S0pYUrCDP09cwMSvozfGS08zRucezFUn9aRtU03SF6kuCgdJqvXb9vDA5EW8Mm0lJZU0xhvRty3XjehNjzaNE1SdSN2lcJCk2L6nKNQYbxm7i6I3xsvt3IKbTu3NEZ3VGE8kUVIqHMzsduC2cpvXuXu7JJQj1aCwuJQX81bw0AeL2bQzemO8Hm0ac8PI3pzUp40a44kkWEqFQ8gC4Pgyr6P/Wik1Qmmp89ZXa7hv0gJWbtoddd+2TetxzcmH8LOBaownkiypGA7F7h79rqTUKJ8u2sDdE+cxd/W2qPs1qZ/Br47vzgVHdaVBlh5LFUmmVAyHbma2hqAtRx7wO3dfmuSaZD/MXb2VeybO57+LNkTdLys9jXOP7Mzlw3vQQo3xRFJCqoVDHnA+MB9oA9wMTDGzvu6+sfzOZnYJcAlAp06dElimRLNy0y7+MmkBb365Jup+ZvCT/h25+uRD1BhPJMWYV9bWMonMrDGwFLjb3e+Ptm9ubq5Pnz49MYVJRJt2FvLwB4t5YWrljfGOO6Q1N4zszaEdmiaoOhGJxMxmuHtu+e2pdubwPe6+w8y+Bnomuxap2O7CEp75bBmPfbSE7XujN8br17EZN43qzVE9shNUnYjsj5QOBzOrD/QGPkx2LRKuuKSU/52xir++t5D126N3bu/UsiHXjejFaf3aqzGeSA2QUuFgZvcBbwHfEtxzuAVoBDyXzLrk+9ydSd+s496J81lSsDPqvi0bZXHlCT0YM6QzWRl6LFWkpkipcAAOAl4CsoECYCow1N1XJLUqAYJQmLZ8M/dMnM+MFZuj7tsgM52xx3Rl7LHdaKLGeCI1TkqFg7ufkewa5Pvcna9WbWXC3LVMnJvP8o27ou6fnmacMehgfnNST9o0UWM8kZoqpcJBUkNpqTPz2828M2ct7369ltVbos9o3mdUTjuuG9GLbq3VGE+kplM4CBDcXP5i2SYmzA0CobIbzGUN7tqSG0f1ZmCnFtVYoYgkksKhDissLmXKkg1MmLOW9+atq7QRXnmHtA0a453QW43xRGobhUMds6eohE8WFjBxbhAI2/dEn5dQnhkM6tyS0wcdzI8HdCRdj6WK1EoKhzpg595iPlywnglz1/Lh/PXsKqxao9v0NOPIbq0YmdOOU/q21Y1mkTpA4VBLbdtTxOR565gwZy0fLyxgb3H0dhblZaYbw3pkMyqnPScf2lYN8UTqGIVDLbJpZyHvfbOWCXPX8tniDRSVVK1vVr2MNI7v1ZpROe05oU8bmmp+gkidpXCo4dZv38O7X69jwpx88pZtqnQd5vIaZaUzvHcbTu3XnuN7taZhlr4lREThUCOt3rKbiaFJadNXbKaqjXWb1s/gpEPbMiqnPcf0zKZ+phbWEZHvUzjUEMs37Py/WcqzV22t8ue3apTFKX3bMjKnPUd2a6U+RyISlcIhhS1at50Jc4N7CPPyoy+xGUmbJvUYmdOOkTntGNylpdZjFpGYKRxSiLvzTf42JsxZy4S5+ZV2PI2kY/MGjMppx6h+7RhwcAu1xxaR/aJwSDJ358uVW5gYOkP4dlP0xnaRdM1uFARCTntyOjbVbGUROWAKhyQoKXWmL/+uj1H+1j1VPkavtk0YGTpD6NW2iQJBROJK4ZAgRSWl5C3dxIS5+bz79To27Ii9sd0+/To2CwIhp506n4pItVI4VKO9xSV8tvi7xnZbdhVV+RgDOzVnVE57Rua04+CWDauhShGRcAqHONtdWMLHCwuYODefyfPWs31v1RrbpVnQAntUTntG9G1Hu2bqYyQiiadwiIMde4v5YP56Js7N58P5Bewuqlpju4w046ge2YzKacfJh7Ylu3G9aqpURCQ2Cof9tHVXEe/PW8eEufl8smgDhVVsbJeVkcaxPbMZmdOek/u0pVlD9TESkdShcKiCjTv2MumbdUyYu5YpizdQXMU+Rg0y0xneuzUjc9pzQu82NK6nL7+IpCb9dKrEum17QnMQ8vli2SaqmAc0rpfBiX3aMCqnPccd0poGWepjJCKpT+EQwcpNu3j362BS2owVm6v8+c0bZnJyn7aM6teOo3tkUy9DgSAiNYvCIWRpwY5QY7u1zFld9cZ22Y3rMaJv0Ol0SLeWZKqPkYjUYHU2HNydhet2MGFuPhPnrmX+2u1VPkb7ZvUZ0TeYlJbbpaXWUxaRWqPOhsPpT0zli2Wbqvx5nVo2ZFSo0+nhBzVXYzsRqZXqbDh0b9045nDo3roRp/YLZikf2l6N7USk9quz4TAqpx0vffFtheN92jcNdTptR8+2TRJYmYhI8tXZcDiyeyuaNchk6+7v+h0dfnDz4JJR33Z0yW6UxOpERJKrzoZDZnoapxzaluUbdzIy1NiuY/MGyS5LRCQl1NlwALjnZ4fphrKISAQp+TC+mV1mZsvMbI+ZzTCzY6rjfRQMIiKRpVw4mNnpwIPAncAAYAowwcw6JbUwEZE6JOXCAbgGGOfuT7r7PHe/AsgHfpXkukRE6oyUCgczywKOACaVG5oEHJX4ikRE6qaUCgcgG0gH1pXbvg5oV35nM7vEzKab2fSCgoJE1CciUiekWjhUibs/4e657p7bunXrZJcjIlJrpNqjrBuAEqBtue1tgbXRPnHGjBkbzGzFfr5vdui9JTb6elWNvl5Vo69X1R3I16xzpI0pFQ7uXmhmM4CTgf8tM3Qy8Holn7vfpw5mNt3dc/f38+safb2qRl+vqtHXq+qq42uWUuEQcj/wvJl9AXwGXAp0AB5LalUiInVIyoWDu79iZq2Am4H2wFzgVHff30tGIiJSRSkXDgDu/ijwaALf8okEvldtoK9X1ejrVTX6elVd3L9m5u7xPqaIiNRwNfpRVhERqR4KBxERCaNwEBGRMHU2HMzsWDP7t5mtNjM3s/OTXVMqM7ObzGyamW0zswIze8vMcpJdV6oys8vN7KvQ12ubmX1uZqclu66aIvT95mb2cLJrSUVmdnvo61P2I+pE4aqqs+EANCZ4TPY3wO4k11ITHE/wBNlRwAlAMfC+mbVMZlEpbBVwAzAQyAU+AN40s8OSWlUNYGZDgUuAr5JdS4pbQPC4/76PfvE8eEo+ypoI7v4O8A6AmY1LbjWpz91HlH1tZucAW4GjgbeSUlQKc/d/ldv0ezP7FXAk+qFXITNrBrwIXAjcluRyUl2xu8f1bKGsunzmIAemCcH3z+ZkF5LqzCzdzM4gOFudkux6UtwTwGvu/mGyC6kBupnZmtCqmS+bWbd4HrzOnjnIAXsQ+BL4PMl1pCwz60fw9akP7AB+4u5zkltV6jKzsUAP4Oxk11ID5AHnA/OBNgQdJaaYWV933xiPN1A4SJWZ2f3AMGCYu5cku54UtgDoDzQDfg48Z2bHu/vcpFaVgsysF8HSwMPcvSjZ9aQ6d59Q9rWZTQWWAucR9Kc7YAoHqRIz+ytwBjDc3Zcmu55U5u6FwOLQyxlmNgi4GrgoeVWlrCMJ2k5/bWb7tqUDx5rZpUAjd9+brOJSnbvvMLOvgZ7xOqbCQWJmZg8CpxMEw/xk11MDpQH1kl1EinoTmF5u27PAIoIzisJEF1STmFl9oDcQt3s1dTYczKwxwfVNCP7RdjKz/sAmd/82aYWlKDN7BDgH+DGw2cz2Ldu6w913JK2wFGVmdwP/AVYS3LwfQ/A4sOY6RODuW4AtZbeZ2U6Cf4+6DFeOmd1H8JTgtwT3HG4BGgHPxes96vLTSrnArNBHA+CO0H//IZlFpbDLCH7ITQbyy3xcm8yiUlg74AWC+w6TgUHAqPLXikX200HASwTfX28Ae4Gh8VzaQF1ZRUQkTF0+cxARkQooHEREJIzCQUREwigcREQkjMJBRETCKBxERCSMwkGkmoQWZNEELqmRFA4iEYRWCZxcwVif0MpbpyS6LpFEUTiIRPY0MNzMukQYuwhYAbyf0IpEEkjhIBLZf4B1wAVlN5pZJkGPqWeBJ0MLrew2s0Vmdr2ZVfhvyszGmdnb5baFXXoyswvM7Bsz22NmC83s6mjHFakO+oYTicDdiwmamJ1f7gfzDwhaSz8DrAZGA32A3wO/o1yYVFVowZs7gVtDx/0twVrUlx3IcUWqSuEgUrGngU7ASWW2XQRMcveV7n6ru09z9+Xu/irwGHDmAb7nLcD17v6auy9z97eAu1E4SILV2ZbdIpVx90Vm9jHBYveTzKwDMIJgsSNCi9BcDHQm6OybSXAvYr+YWWvgYOBxM/t7maEMwCJ/lkj1UDiIRPc0wb2FlgRr9m4C/mVmpwMPELQsnwJsAy4HfhLlWKWE/5DPLPPf+87kLw0dUyRpFA4i0b0GPESw6P2FwD/cvcjMhgF57v7wvh3NrHslxyogWFO6rP977e7rzGwN0N3d/xGH2kX2m8JBJAp3321m44HbgRYEZxIACwluVo8iWCf6DOA4YHOUw30AXG9mFwKfAD8FjgZWldnnNuAhM9sCvENwZjEQ6Ojud8XpryVSKd2QFqncUwTBMMXd54W2PQ68CowHpgFdgL9EO4i7v0uw4uCfgBmhz3m03D5PEZyhnAPMBv4LXAIsi8vfRCRGWglORETC6MxBRETCKBxERCSMwkFERMIoHEREJIzCQUREwigcREQkjMJBRETCKBxERCTM/wcAYFCOGbvErAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "input_values = [1, 2, 3, 4, 5]\n",
    "squares = [1, 4, 9, 16, 25]\n",
    "plt.plot(input_values, squares, linewidth=5)  # 注意x，y向量维度，大小要完全一致，否则报错\n",
    "\n",
    "# 设置图标标题，并给坐标轴添加标签\n",
    "plt.title(\"$y=x^2$\")  # letex语法\n",
    "plt.xlabel(\"Value\", fontsize=14)\n",
    "plt.ylabel(\"Square Of Value\", fontsize=14)\n",
    "# 设置刻度标记大小\n",
    "plt.tick_params(axis='both', labelsize=14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用plot()时可以指定各种实参，实现更复杂的变形。但是由于篇幅关系，不再一一赘述，你可以通过help()函数了解更多"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function plot in module matplotlib.pyplot:\n",
      "\n",
      "plot(*args, scalex=True, scaley=True, data=None, **kwargs)\n",
      "    Plot y versus x as lines and/or markers.\n",
      "    \n",
      "    Call signatures::\n",
      "    \n",
      "        plot([x], y, [fmt], *, data=None, **kwargs)\n",
      "        plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs)\n",
      "    \n",
      "    The coordinates of the points or line nodes are given by *x*, *y*.\n",
      "    \n",
      "    The optional parameter *fmt* is a convenient way for defining basic\n",
      "    formatting like color, marker and linestyle. It's a shortcut string\n",
      "    notation described in the *Notes* section below.\n",
      "    \n",
      "    >>> plot(x, y)        # plot x and y using default line style and color\n",
      "    >>> plot(x, y, 'bo')  # plot x and y using blue circle markers\n",
      "    >>> plot(y)           # plot y using x as index array 0..N-1\n",
      "    >>> plot(y, 'r+')     # ditto, but with red plusses\n",
      "    \n",
      "    You can use `.Line2D` properties as keyword arguments for more\n",
      "    control on the appearance. Line properties and *fmt* can be mixed.\n",
      "    The following two calls yield identical results:\n",
      "    \n",
      "    >>> plot(x, y, 'go--', linewidth=2, markersize=12)\n",
      "    >>> plot(x, y, color='green', marker='o', linestyle='dashed',\n",
      "    ...      linewidth=2, markersize=12)\n",
      "    \n",
      "    When conflicting with *fmt*, keyword arguments take precedence.\n",
      "    \n",
      "    \n",
      "    **Plotting labelled data**\n",
      "    \n",
      "    There's a convenient way for plotting objects with labelled data (i.e.\n",
      "    data that can be accessed by index ``obj['y']``). Instead of giving\n",
      "    the data in *x* and *y*, you can provide the object in the *data*\n",
      "    parameter and just give the labels for *x* and *y*::\n",
      "    \n",
      "    >>> plot('xlabel', 'ylabel', data=obj)\n",
      "    \n",
      "    All indexable objects are supported. This could e.g. be a `dict`, a\n",
      "    `pandas.DataFrame` or a structured numpy array.\n",
      "    \n",
      "    \n",
      "    **Plotting multiple sets of data**\n",
      "    \n",
      "    There are various ways to plot multiple sets of data.\n",
      "    \n",
      "    - The most straight forward way is just to call `plot` multiple times.\n",
      "      Example:\n",
      "    \n",
      "      >>> plot(x1, y1, 'bo')\n",
      "      >>> plot(x2, y2, 'go')\n",
      "    \n",
      "    - Alternatively, if your data is already a 2d array, you can pass it\n",
      "      directly to *x*, *y*. A separate data set will be drawn for every\n",
      "      column.\n",
      "    \n",
      "      Example: an array ``a`` where the first column represents the *x*\n",
      "      values and the other columns are the *y* columns::\n",
      "    \n",
      "      >>> plot(a[0], a[1:])\n",
      "    \n",
      "    - The third way is to specify multiple sets of *[x]*, *y*, *[fmt]*\n",
      "      groups::\n",
      "    \n",
      "      >>> plot(x1, y1, 'g^', x2, y2, 'g-')\n",
      "    \n",
      "      In this case, any additional keyword argument applies to all\n",
      "      datasets. Also this syntax cannot be combined with the *data*\n",
      "      parameter.\n",
      "    \n",
      "    By default, each line is assigned a different style specified by a\n",
      "    'style cycle'. The *fmt* and line property parameters are only\n",
      "    necessary if you want explicit deviations from these defaults.\n",
      "    Alternatively, you can also change the style cycle using\n",
      "    :rc:`axes.prop_cycle`.\n",
      "    \n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    x, y : array-like or scalar\n",
      "        The horizontal / vertical coordinates of the data points.\n",
      "        *x* values are optional and default to ``range(len(y))``.\n",
      "    \n",
      "        Commonly, these parameters are 1D arrays.\n",
      "    \n",
      "        They can also be scalars, or two-dimensional (in that case, the\n",
      "        columns represent separate data sets).\n",
      "    \n",
      "        These arguments cannot be passed as keywords.\n",
      "    \n",
      "    fmt : str, optional\n",
      "        A format string, e.g. 'ro' for red circles. See the *Notes*\n",
      "        section for a full description of the format strings.\n",
      "    \n",
      "        Format strings are just an abbreviation for quickly setting\n",
      "        basic line properties. All of these and more can also be\n",
      "        controlled by keyword arguments.\n",
      "    \n",
      "        This argument cannot be passed as keyword.\n",
      "    \n",
      "    data : indexable object, optional\n",
      "        An object with labelled data. If given, provide the label names to\n",
      "        plot in *x* and *y*.\n",
      "    \n",
      "        .. note::\n",
      "            Technically there's a slight ambiguity in calls where the\n",
      "            second label is a valid *fmt*. ``plot('n', 'o', data=obj)``\n",
      "            could be ``plt(x, y)`` or ``plt(y, fmt)``. In such cases,\n",
      "            the former interpretation is chosen, but a warning is issued.\n",
      "            You may suppress the warning by adding an empty format string\n",
      "            ``plot('n', 'o', '', data=obj)``.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    list of `.Line2D`\n",
      "        A list of lines representing the plotted data.\n",
      "    \n",
      "    Other Parameters\n",
      "    ----------------\n",
      "    scalex, scaley : bool, default: True\n",
      "        These parameters determine if the view limits are adapted to the\n",
      "        data limits. The values are passed on to `autoscale_view`.\n",
      "    \n",
      "    **kwargs : `.Line2D` properties, optional\n",
      "        *kwargs* are used to specify properties like a line label (for\n",
      "        auto legends), linewidth, antialiasing, marker face color.\n",
      "        Example::\n",
      "    \n",
      "        >>> plot([1, 2, 3], [1, 2, 3], 'go-', label='line 1', linewidth=2)\n",
      "        >>> plot([1, 2, 3], [1, 4, 9], 'rs', label='line 2')\n",
      "    \n",
      "        If you make multiple lines with one plot call, the kwargs\n",
      "        apply to all those lines.\n",
      "    \n",
      "        Here is a list of available `.Line2D` properties:\n",
      "    \n",
      "        Properties:\n",
      "        agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array\n",
      "        alpha: float or None\n",
      "        animated: bool\n",
      "        antialiased or aa: bool\n",
      "        clip_box: `.Bbox`\n",
      "        clip_on: bool\n",
      "        clip_path: Patch or (Path, Transform) or None\n",
      "        color or c: color\n",
      "        contains: unknown\n",
      "        dash_capstyle: {'butt', 'round', 'projecting'}\n",
      "        dash_joinstyle: {'miter', 'round', 'bevel'}\n",
      "        dashes: sequence of floats (on/off ink in points) or (None, None)\n",
      "        data: (2, N) array or two 1D arrays\n",
      "        drawstyle or ds: {'default', 'steps', 'steps-pre', 'steps-mid', 'steps-post'}, default: 'default'\n",
      "        figure: `.Figure`\n",
      "        fillstyle: {'full', 'left', 'right', 'bottom', 'top', 'none'}\n",
      "        gid: str\n",
      "        in_layout: bool\n",
      "        label: object\n",
      "        linestyle or ls: {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}\n",
      "        linewidth or lw: float\n",
      "        marker: marker style string, `~.path.Path` or `~.markers.MarkerStyle`\n",
      "        markeredgecolor or mec: color\n",
      "        markeredgewidth or mew: float\n",
      "        markerfacecolor or mfc: color\n",
      "        markerfacecoloralt or mfcalt: color\n",
      "        markersize or ms: float\n",
      "        markevery: None or int or (int, int) or slice or List[int] or float or (float, float) or List[bool]\n",
      "        path_effects: `.AbstractPathEffect`\n",
      "        picker: unknown\n",
      "        pickradius: float\n",
      "        rasterized: bool or None\n",
      "        sketch_params: (scale: float, length: float, randomness: float)\n",
      "        snap: bool or None\n",
      "        solid_capstyle: {'butt', 'round', 'projecting'}\n",
      "        solid_joinstyle: {'miter', 'round', 'bevel'}\n",
      "        transform: `matplotlib.transforms.Transform`\n",
      "        url: str\n",
      "        visible: bool\n",
      "        xdata: 1D array\n",
      "        ydata: 1D array\n",
      "        zorder: float\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    scatter : XY scatter plot with markers of varying size and/or color (\n",
      "        sometimes also called bubble chart).\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    **Format Strings**\n",
      "    \n",
      "    A format string consists of a part for color, marker and line::\n",
      "    \n",
      "        fmt = '[marker][line][color]'\n",
      "    \n",
      "    Each of them is optional. If not provided, the value from the style\n",
      "    cycle is used. Exception: If ``line`` is given, but no ``marker``,\n",
      "    the data will be a line without markers.\n",
      "    \n",
      "    Other combinations such as ``[color][marker][line]`` are also\n",
      "    supported, but note that their parsing may be ambiguous.\n",
      "    \n",
      "    **Markers**\n",
      "    \n",
      "    =============    ===============================\n",
      "    character        description\n",
      "    =============    ===============================\n",
      "    ``'.'``          point marker\n",
      "    ``','``          pixel marker\n",
      "    ``'o'``          circle marker\n",
      "    ``'v'``          triangle_down marker\n",
      "    ``'^'``          triangle_up marker\n",
      "    ``'<'``          triangle_left marker\n",
      "    ``'>'``          triangle_right marker\n",
      "    ``'1'``          tri_down marker\n",
      "    ``'2'``          tri_up marker\n",
      "    ``'3'``          tri_left marker\n",
      "    ``'4'``          tri_right marker\n",
      "    ``'s'``          square marker\n",
      "    ``'p'``          pentagon marker\n",
      "    ``'*'``          star marker\n",
      "    ``'h'``          hexagon1 marker\n",
      "    ``'H'``          hexagon2 marker\n",
      "    ``'+'``          plus marker\n",
      "    ``'x'``          x marker\n",
      "    ``'D'``          diamond marker\n",
      "    ``'d'``          thin_diamond marker\n",
      "    ``'|'``          vline marker\n",
      "    ``'_'``          hline marker\n",
      "    =============    ===============================\n",
      "    \n",
      "    **Line Styles**\n",
      "    \n",
      "    =============    ===============================\n",
      "    character        description\n",
      "    =============    ===============================\n",
      "    ``'-'``          solid line style\n",
      "    ``'--'``         dashed line style\n",
      "    ``'-.'``         dash-dot line style\n",
      "    ``':'``          dotted line style\n",
      "    =============    ===============================\n",
      "    \n",
      "    Example format strings::\n",
      "    \n",
      "        'b'    # blue markers with default shape\n",
      "        'or'   # red circles\n",
      "        '-g'   # green solid line\n",
      "        '--'   # dashed line with default color\n",
      "        '^k:'  # black triangle_up markers connected by a dotted line\n",
      "    \n",
      "    **Colors**\n",
      "    \n",
      "    The supported color abbreviations are the single letter codes\n",
      "    \n",
      "    =============    ===============================\n",
      "    character        color\n",
      "    =============    ===============================\n",
      "    ``'b'``          blue\n",
      "    ``'g'``          green\n",
      "    ``'r'``          red\n",
      "    ``'c'``          cyan\n",
      "    ``'m'``          magenta\n",
      "    ``'y'``          yellow\n",
      "    ``'k'``          black\n",
      "    ``'w'``          white\n",
      "    =============    ===============================\n",
      "    \n",
      "    and the ``'CN'`` colors that index into the default property cycle.\n",
      "    \n",
      "    If the color is the only part of the format string, you can\n",
      "    additionally use any  `matplotlib.colors` spec, e.g. full names\n",
      "    (``'green'``) or hex strings (``'#008000'``).\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(plt.plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15.2.3 使用scatter()绘制散点图并设置样式"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有时候，需要绘制散点图并设置各个数据点的样式。例如，你可能想以一种颜色显示较小的值，而用另一种颜色显示较大的值。绘制大型数据集时，你还可以对每个点都设置同样的样式，再使用不同的样式选项重新绘制某些点，以突出它们。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "要绘制单个点，可使用函数scatter() ，并向它传递一对 x 和 y 坐标，它将在指定位置绘制一个点："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "plt.scatter(2, 4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面来设置输出的样式，使其更有趣：添加标题，给轴加上标签，并确保所有文本都大到能够看清："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "plt.scatter(2, 4, s=200)  # s指定点的大小size\n",
    "\n",
    "# 设置图表标题并给坐标轴加上标签\n",
    "plt.title(\"Square Numbers\", fontsize=24)\n",
    "plt.xlabel(\"Value\", fontsize=14)\n",
    "plt.ylabel(\"Square of Value\", fontsize=14)\n",
    "\n",
    "# 设置刻度标记的大小\n",
    "plt.tick_params(axis='both', which='major', labelsize=14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15.2.4 使用scatter绘制一系列点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_values = [1, 2, 3, 4, 5]\n",
    "y_values = [1, 4, 9, 16, 25]\n",
    "plt.scatter(x_values, y_values, s=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "列表**x_values**包含要计算其平方值的数字，而列表**y_values**包含前述每个数字的平方值。将这些列表传递给scatter() 时，matplotlib依次从每个列表中读取一个值来绘制一个点。要绘制的点的坐标分别为 (1, 1)、(2, 4)、(3, 9)、(4, 16)和(5, 25)，最终的结果如图所示。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15.2.5 自动计算数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_values = list(range(1, 1001))\n",
    "y_values = [i**2 for i in x_values]  # 自动计算数据\n",
    "\n",
    "plt.scatter(x_values, y_values, s=40)\n",
    "\n",
    "# 设置图表标题并给坐标轴加上标签\n",
    "plt.title(\"Square Numbers\", fontsize=24)\n",
    "plt.xlabel(\"Value\", fontsize=14)\n",
    "plt.ylabel(\"Square of Value\", fontsize=14)\n",
    "\n",
    "# 设置刻度标记的大小\n",
    "plt.tick_params(axis='both', which='major', labelsize=14)\n",
    "\n",
    "# 设置每个坐标轴的取值范围\n",
    "plt.axis([0, 1100, 0, 1100000])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们首先创建了一个包含 x 值的列表，其中包含数字1~1000。接下来是一个生成 y 值的列表解析，它遍历 x 值（for x in x_values ），计算其平方值，\n",
    "并将结果存储到列表y_values 中。然后，将输入列表和输出列表传递给scatter() 。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于这个数据集较大，我们将点设置得较小，并使用函数axis() 指定了每个坐标轴的取值范围。函数axis() 要求提供四个值：x 和 y 坐标轴的最小值和最大值。在这里，我们将 x 坐标轴的取值范围设置为0\\~1100，并将 y 坐标轴的取值范围设置为0\\~1 100 000。结果如图所示。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15.2.6 删除数据点的轮廓"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "matplotlib允许你给散点图中的各个点指定颜色。默认为蓝色点和黑色轮廓，在散点图包含的数据点不多时效果很好。但绘制很多点时，黑色轮廓可能会粘连在一起。要删除数据点的轮廓，可在调用scatter() 时传递实参 **edgecolor='none' ：**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_values, y_values, edgecolor='none', s=40)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15.2.7 自定义颜色"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "要修改数据点的颜色，可向scatter() 传递参数**color** ，并将其设置为要使用的颜色的名称，如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_values, y_values, color='red', edgecolor='none', s=40)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "你还可以使用RGB颜色模式自定义颜色。要指定自定义颜色，可传递参数c ，并将其设置为一个元组，其中包含三个0~1之间的小数值，它们分别表示红色、绿色和蓝色分量。例如，下面的代码行创建一个由淡蓝色点组成的散点图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_values, y_values, color=(100/255, 149/255, 237/255), edgecolor='none', s=40)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15.2.8 使用颜色映射"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**颜色映射 （colormap）** 是一系列颜色，它们从**起始颜色渐变到结束颜色**。在可视化中，颜色映射用于**突出数据的规律**，例如，你可能用较浅的颜色来显示较小的值，并使用较深的颜色来显示较大的值。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "模块pyplot 内置了一组颜色映射。要使用这些颜色映射，你需要告诉pyplot 该如何设置数据集中每个点的颜色。下面演示了如何根据每个点的 y 值来设置其颜色："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x15e7f278520>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mMHtcHXdcfkqpyqwK+QT6CmCemc0xs+HAZcDSjHN+Q9A7x8wmEgzBrC9cmSIyFHX39HDJj57JHmYhOsxizBpfrzAvgn4D3d27gauBB4G1wN3uvtrMbjSzi8LTHgR2mtka4FHg79x952AVLSKlF+/u4f0/WZn10FBymCX838xx9fzH+zXMUgzmnnk5iqOpqcmbm7MX6hGRoa+zq5sP/XwVvb3BVER3x0ntBZrIlelj67hNN0ALysyedvemXO9pgz4RGZCDnd185BfPJo8TwyqJ18GPxsyx9QrzItOj/yKSt73tXXzi7tXJ4+i2cTGSA+jMGlfPLe/RE6DFpkAXkbzsONDJZ+5bmzy2yP9FdxuaO76eb+px/pJQoItIvzbubuOLv3sxrS3Xps7HThzB196phbZKRYEuIofUsv0gNzzUAoTj5RiEN0FjqXmKzJ/cwJcvOK50hYoCXUT6trJ1L7f86ZW0tqBDbqkpLcAp00Zy/Vv0LGGpKdBFJKfHX9rJD59Kfyg8mM3iRBY55+xZY/jMG+cUv0DJokAXkSy/W72Ne5/dCoS57alOeXQFxYVHj+NvztYeoEOFAl1E0vxncyuPvbQHSN34jJnRG5nJAvD2EyZy2WmZC69KKSnQRSTptv99mWdeO5DVnjbPHLj05Mm8ff6UYpYmeVCgiwgANz/cwvpdncnj5A6gyW55cBP0Y2dM46+PnlD0+qR/CnQR4UvLnmdne09knBzAiIXTEwEw45pzZnDKjLElq1MOTYEuUuWuW7qWtu7UFMTMB4YS/fN/OG82cyeNLEWJkicFukgV++xv1hLPWHDVcjzOf9OiYzhqdH2xy5MBUqCLVKF4Tw/X3f8ivRlhHgt750RWUPy3dxzLqPraElQpA6VAF6kyuw528s9/3JA8Tt38jMwzD9v//V3HMbympkSVykAp0EWqyEs7DnLrnzcmjy1M8cx55jXA995zYmmKlMOmQBepEk++spt7n92WPI4ux2JATZjm9TXGNy86vgQVypFSoItUgV//ZTNPtO4DUuuxuAf7f/ZGHhgaWxfjxguOLVGVcqQU6CIV7o4/v8yGPV3J48SYeSzsocfCPJ81djjXvnFuSWqUwlCgi1SwbzzSwt6OXiD95meM1Ji5Aa+fNooPvl7rspQ7BbpIhbrhwReJh/MSE2uYWzjUYpYaM7/g2Amcd+zE0hUqBaNAF6kwHfEebn50Pb2RttQwi+GkHue/8rSpnDh1dPGLlEGhQBepIK2727iz+bXksUV65THC5bXCeeafP3cmk/T0Z0VRoItUiOUv7+Shlt1Z7ZE9nIONKoAvn3809bV6YKjSKNBFKsA9Kzfx/M725HHiSc/E/yXyvKYGvvpW7f1ZqRToImXutv9dz97O9EVZEkMtibFzgFF1Ma5beHTR65PiUaCLlCl351uPracnkuWJXnkwvzy1YuKssXVceYb2/qx0CnSRMrS3rZMfrNiUMZPFwDycbB62mfGGmaN587GTS1GmFJkCXaTMrNuyl2Uv7EytwxI+HBSsehuEemLM/NIFU5g3WZtSVAsFukgZ+f3aLazbEdz8DII8HFYh9Sh/4hbop94wg9EjhpewWim2WD4nmdkiM3vezFrM7PpDnPdeM3MzaypciSIC8KOnXuGFyEyWBDMLHuc3qInBsBh84U1zFOZVqN8eupnVALcC5wOtwAozW+ruazLOGwVcAywfjEJFqlVPby93PPEqPXjWe4leeWIh85G1xifPmVPkCmWoyKeHfgbQ4u7r3b0LuAu4OMd5/wx8A+goYH0iVW3bgQ7uePLVtChPzDE3S33FDI4ZV68wr3L5BPo0YGPkuDVsSzKz04AZ7v67Q/1CZrbYzJrNrHn79u0DLlakmix/dRf3Pbslq93C/T5j4SP8MYO3zBvPu09+XfGLlCHliG+KmlkM+DZwZX/nuvsSYAlAU1NT9r8fRQSAe/+yiZ3t3WltMYxe87Q1zDHjQ6dMZfxIrcki+QX6JmBG5Hh62JYwCjgJeCzcJXwKsNTMLnL35kIVKlINent7+UlzK92e6u+kPSyE4RaslhgDPnn2LGKxvOY2SBXIJ9BXAPPMbA5BkF8GfCDxprvvBZKLKZvZY8B1CnORgdm6r51lz2/PWvY2tTNFalrimDrjw02zSlKnDF39Brq7d5vZ1cCDBJuB3+nuq83sRqDZ3ZcOdpEile6JDTt5fsfB5HFqd6FkiifD/fgJDSzUk5+SQ15j6O6+DFiW0fblPs5deORliVSPX61spa079+Jawe5CqdUSFx03gZnj9OSn5KYnRUVKpCPezT2rttCbMV6e6JRHdxcy4Iqm6dTWaA1z6ZsCXaQEnt+6l+bW/UD68EriMf5klxyjYRh84DStlCj9U6CLFNmyNZvZ09GT1pY5Zp7I82MmNXDuHG3gLPlRoIsUSXdPD/eu2pL11CeRnnky1IG3HjORqWNHFL9QKVsKdJEieHnnAZ7auC+tLTFWDpHNKAjml1922jRqNL9cBkiBLjLIHl63jV0d6U99WmQ3ITzVNqY+xjvm6xF+OTwKdJFBEu/p4f7ntqUPsYTd8rSboOGA+anTRnHCUWNKUapUCAW6yCB4Yet+Vm892Of7MSIzW4B3zT+KEcP111GOjP4EiRTYQ2u30hbPflAo2jOH4IboiBrjnQumFr9IqUgKdJEC2dfWyf+8tIfejI0osh7jDx0/qYEF08YWtUapbAp0kQJ4+tXdbN7XldWeGCdPDLEkBswvPGESDRpikQLTnyiRI9DT08Mf1u2kJ9IpjyyOmPUEaOMwY9H8KSWpVSqfAl3kML2y4wBrt7anLXcLwfK2Fm5EkdqHwjhh8giOm6JZLDJ4FOgih+GRdduJZyY5kYW1IhtRGLDo+IkMr9VfNxlc+hMmMgA79rXzzKaDGbc9A+GCt8l55bgxrj7G/5s3qZglShVToIvk6c8v7aQ9nljQNiW6RVxq5XI4fcZIjhrbUNwipaop0EX6sa+tk+ZXDwRrk+fomlvi9meY5cOAt87XjkJSfAp0kUNo3rCb/V2pwXIzME8fWkn0zA2YNX44J0zVjU8pDQW6SA772rtYufEAvbkGyyPMklPLWXjMON34lJLSnz6RDE+/vJu27GeEkjc9YwbulhxJnzCihtNmjytqjSK5KNBFQnvaunhu08Hc4+SRm56J4RYDzpw9hpH1tUWvVSQXBboI0Lx+D5092UmeWEYrEeKJUG8cbpw5d3yxyxQ5JAW6VLVte9pZv6MzY83y1BpaqQ0oLHl86vRGxjTWFblSkf4p0KUq9fb20rxhf58rIwav04dX6mrgzKPVK5ehS4EuVadl6352HcyM8owwjzztCbBgWiNjG4cXqUKRw6NAl6rR3hnnuU1tOPRx4zN9vXIHGoejGSxSNhToUhVWvbKPzsik8rTsNjBPLXWb6JwvmN6oGSxSVhToUtE27T7I5j09fb6fGCdPhDrAqHpj/nQ97SnlR4EuFamzK87q19r7WBUx0RtPX1DLzTlt5kg97SllS39ypeI8++p+4rkGyYHoOlqJUAeYPGoYsyc1FqlCkcERy+ckM1tkZs+bWYuZXZ/j/c+Z2RozW2VmD5vZrMKXKnJor2w/wMpX99OdkeXJGSvJhtQPNQanzxmtMJeK0G8P3cxqgFuB84FWYIWZLXX3NZHTngGa3L3NzD4J/Cvw/sEoWCTTgbYuXgofDuqrYw6RoZWwi37MxHrGjdIDQlI58hlyOQNocff1AGZ2F3AxkAx0d380cv6TwIcKWaRILt3d3azZ3JG14URU5tCKO4yqM45/3eii1ChSTPkE+jRgY+S4FTjzEOdfBfx3rjfMbDGwGGDmzJl5liiSbW3rAeJO9u5BGdMRI/tOAHDyjAaGDdOtI6lMBf2TbWYfApqAN+Z6392XAEsAmpqa+llpWiTbhu0HONjJIfrkgejMFYBZ4+uYoOEVqXD5BPomYEbkeHrYlsbM3gL8I/BGd+8sTHkiga172tl+sOeQSZ45vAIwarhxzJRRg12eyJCQT6CvAOaZ2RyCIL8M+ED0BDM7FbgDWOTu2wpepVStvQc72bS3u8+bndEHgzKHVxZMbyQWy2sil0hF6DfQ3b3bzK4GHgRqgDvdfbWZ3Qg0u/tS4JvASOBXFnSPXnX3iwaxbqlw7R1dvLwrfsiZK5mbTiTSfN6kOhrqtZCWVJ+8xtDdfRmwLKPty5HXbylwXVKlOrq6eHlnd98zV3I8GJQI9cmja5gyZkQRqxUZWnS7X4aEeDzO+p1xPDFzJZLnGYsgptrDHxuHw9yjRhalTpGhTIEuJRWPx9mwM97vrBXIHlqpAY5/XSOW9SioSHVSoEtJxONxXt41sKEVILnhxHFH1Wk+uUgG/Y2QouqMx9m4uzs1tJJDXzNXHJg7oZYRuuEpkpMCXYriYFcXW/b05DW0AtnDK9PH1jJqhIJc5FAU6DKo9h7sZEdbb7+PduZac8UwJo+qYfxIPeEpkg8FugyKbXs7OBjvf/XDvoZWJoyMMWlUfREqFakcCnQpqI27OugJO+SHCvMsYZqPG1HDpNHqkYscDgW6HLF4PM5r+3qT88dz5nhiTCVxmLH927gGY6J65CJHRIEuh21/Wye7OzjkjJVM0WEVM5jQEGOcxshFCkKBLgO2eU8n3YcYVsn1WH50xgrA5MYYoxsU5CKFpECXvHR0dbHjYJDe3tewSg6ZQytTxsRoGK7phyKDQYEuh7R9bxedvQP/edEhczOYPqaG2traQpYmIhkU6JKlrbOLPe2H7oX3O6ziMCwGM8brRqdIsSjQBQhmquxsC25uDmi6IdnDKvXD4KgxGh8XKTYFepXbuT9OPBxSyXemSkLmsMqEBmisV5CLlIoCvQrtbYvT0Z3/jc2UIMKjj+fXAJNHxTQ+LjIEKNCrxIGObtriqVkq+cgcJ0+1QkMtjB+p2SoiQ4kCvYLtb+umo5cBd8WjAZ65aNbkRlNvXGSIUqBXEHdnb1sP3dF54gMI8+RMFdJDvWEYjFNvXGTIU6CXuXi8h/1dqRuaA52hAqTd3UzMVDFgwkjTrkAiZUR/W8vQgfYe4p4K74HOTklIWy8rHFIZXQcNdRpSESlHCvQy0NHZQ3vPYf7kjFUOIXMTieAG56gRCnGRcqdAH4K6unpp7zn0MMrhDK0khlMcpy4GYxoV4iKVRIE+BLR19NIdvj7SYZSEXMMptQZjG3XJRSqV/nYXWTzeS0dk+CQa2wPpdadPLcw9O8WA4TUweoQus0g10N/0QdTV5XQVqMfdn2ioj6yF+jpdWpFqo7/1BdDV5cQjx5k97bQwL2CuJ25uAowajqYYilQ5JUAe3J14N8E4d1ZYD46+hlES6mLQUF8zSN9dRMpR1Qd6PA49ZAdzziGSPNL7sB7sCWUGdzTUa2PQWBfDot1yEZGIsgv0nh6IF6BbfKjgHdTx7ox54ZnTxBOhPsygoc4wiw1eLSJSUfJKCzNbZGbPm1mLmV2f4/06M/tl+P5yM5td6ELdoaN78MO8KCz7cDgwuj7G6PoYY0bEaKxXb1xEBqbfQDezGuBW4ALgROByMzsx47SrgN3ufgxwC/CNQhcaP9wnJYssVwZbRoLXAI3DYVR9LPlVX6+euIgcmXxS5Aygxd3Xu3sXcBdwccY5FwM/CV/fA7zZCty9PIx9iosi14c0SwV7DeGj9XWx5FdDXYxYTAEuIoWVzxj6NGBj5LgVOLOvc9y928z2AhOAHdGTzGwxsBhg5syZh1lyCeVYFyXRHANqa6CmRsMkIlIaRb0p6u5LgCUATU1NAxrJrjHoKdLYd3R2SVSMYCf7YcMU2iIy9OQT6JuAGZHj6WFbrnNazWwYMAbYWZAKQ7U10NPd/3n5iK42mGwjGB5Jbcaj0BaR8pJPoK8A5pnZHILgvgz4QMY5S4ErgCeA9wGPuBd+Lkn9sCCEu3tSY+oWfsUMavScjYhUsX4DPRwTvxp4kKATe6e7rzazG4Fmd18K/BD4mZm1ALsIQn9QmEFt2c2eFxEZfHlFo7svA5ZltH058roDuKSwpYmIyEBo7pyISIVQoIuIVAgFuohIhVCgi4hUCBuE2YX5fWOz7cArh/nTJ5LxFGoV0GeuDvrM1eFIPvMsd5+U642SBfqRMLNmd28qdR3FpM9cHfSZq8NgfWYNuYiIVAgFuohIhSjXQF9S6gJKQJ+5OugzV4dB+cxlOYYuIiLZyrWHLiIiGRToIiIVouwCvb8Nq8uRmc0ws0fNbI2ZrTaza8L28Wb2BzN7MfxxXNhuZvbd8PdglZmdVtpPcPjMrMbMnjGzB8LjOeFG4y3hxuPDw/ZB34i8GMxsrJndY2brzGytmZ1d6dfZzK4N/1w/Z2a/MLP6SrvOZnanmW0zs+cibQO+rmZ2RXj+i2Z2xUDrKKtAz3PD6nLUDXze3U8EzgI+FX6u64GH3X0e8HB4DMHnnxd+LQZuL37JBXMNsDZy/A3glnDD8d0EG5BDETYiL5LvAL939+OBkwk+e8VeZzObBnwGaHL3kwiW4L6MyrvOPwYWZbQN6Lqa2XjgKwRbfJ4BfCXxH4G8uXvZfAFnAw9Gjr8IfLHUdQ3C5/wtcD7wPDA1bJsKPB++vgO4PHJ+8rxy+iLY/eph4DzgAYK9SnYAwzKvN8F6/GeHr4eF51mpP8MAP+8YYENm3ZV8nUntNzw+vG4PAG+rxOsMzAaeO9zrClwO3BFpTzsvn6+y6qGTe8PqaSWqZVCE/8Q8FVgOHOXum8O3tgBHha8r5ffh34G/J7UB1QRgj7snNhuMfq60jciBxEbk5WQOsB34UTjM9AMza6SCr7O7bwL+DXgV2Exw3Z6msq9zwkCv6xFf73IL9IpmZiOBe4HPuvu+6Hse/Ce7YuaYmtk7gG3u/nSpaymiYcBpwO3ufipwkNQ/w4GKvM7jgIsJ/mP2OqCR7KGJiles61pugZ7PhtVlycxqCcL8v9z9vrB5q5lNDd+fCmwL2yvh9+Ec4CIzexm4i2DY5TvA2HCjcUj/XMnPPFgbkRdBK9Dq7svD43sIAr6Sr/NbgA3uvt3d48B9BNe+kq9zwkCv6xFf73IL9OSG1eFd8csINqgua2ZmBPuyrnX3b0feSmy+TfjjbyPtHwnvlp8F7I38064suPsX3X26u88muI6PuPsHgUcJNhqH7M+c+L0YtI3IB5O7bwE2mtlxYdObgTVU8HUmGGo5y8wawj/nic9csdc5YqDX9UHgrWY2LvyXzVvDtvyV+kbCYdx4uBB4AXgJ+MdS11Ogz3QuwT/HVgErw68LCcYOHwZeBP4IjA/PN4LZPi8BzxLMICj55ziCz78QeCB8PRd4CmgBfgXUhe314XFL+P7cUtd9mJ/1FKA5vNa/AcZV+nUGbgDWAc8BPwPqKu06A78guEcQJ/iX2FWHc12Bj4WfvQX46EDr0KP/IiIVotyGXEREpA8KdBGRCqFAFxGpEAp0EZEKoUAXEakQCnQRkQqhQBcRqRD/H/j0ATtj4icsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x_values = list(range(1001))\n",
    "y_values = [x**2 for x in x_values]\n",
    "plt.scatter(x_values, y_values, c=y_values, cmap=plt.cm.Blues, edgecolor='none', s=40)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们将参数**c**设置成了一个**y值列表**，并使用参数**cmap**告诉pyplot 使用**哪个颜色映射**。这些代码将 y 值较小的点显示为浅蓝色，并将 y 值较大的点显示为深蓝色，如图所示。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15.2.9 自动保存图表"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "要让程序自动将图表保存到文件中，可将对plt.show() 的调用替换为对**plt.savefig()的调用：**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.savefig('squares_plot.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第一个实参指定要以**什么样的文件名**保存图表，这个文件将存储到所在的目录中；第二个实参指定**将图表多余的空白区域裁剪掉**。如果要保留图表周围多余的空白区域，可省略这个实参。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 动手试一试"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**15-1 立方** ：数字的三次方被称为其立方。请绘制一个图形，显示前5个整数的立方值，再绘制一个图形，显示前5000个整数的立方值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "def draw_cube_number(n):\n",
    "    x_values = list(range(n))\n",
    "    y_values = [i ** 3 for i in x_values]\n",
    "    plt.scatter(x_values, y_values, s=10, c=y_values, cmap=plt.cm.Reds)\n",
    "    plt.title('$y=x^3$', fontsize=14)\n",
    "    plt.xlabel('x', fontsize=14)\n",
    "    plt.ylabel('y', fontsize=14)\n",
    "    plt.tick_params(axis='both', labelsize=14)\n",
    "    plt.show()\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_cube_number(5000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 15.3 随机漫步"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在本节中，我们将使用Python来生成随机漫步数据，再使用matplotlib以引人瞩目的方式将这些数据呈现出来。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**随机漫步**是这样行走得到的路径：每次行走都完全是随机的，没有明确的方向，结果是由一系列随机决策决定的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在自然界、物理学、生物学、化学和经济领域，随机漫步都有其实际用途。例如，漂浮在水滴上的花粉因不断受到水分子的挤压而在水面上移动。水滴中的分子运动是随机的，因此花粉在水面上的运动路径犹如随机漫步。我们稍后将编写的代码模拟了现实世界的很多情形。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15.3.1 创建RandomWalk()类"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为模拟随机漫步，我们将创建一个名为RandomWalk 的类，它随机地选择前进方向。这个类需要三个属性，其中一个是存储随机漫步次数的变量，其他两个是列表，分别存储随机漫步经过的每个点的 x 和 y 坐标。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RandomWalk 类只包含两个方法：\\_\\_init__() 和fill_walk() ，其中后者计算随机漫步经过的所有点。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from random import choice\n",
    "\n",
    "class RandomWalk:\n",
    "    def __init__(self, num_points=5000):\n",
    "        self.num_points = num_points\n",
    "        self.x_values = [0]\n",
    "        self.y_values = [0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为做出随机决策，我们将所有可能的选择都存储在一个**列表**中，并在每次做决策时都使用choice() 来决定使用哪种选择。接下来，我们将随机漫步包含的**默认点数**设置为5000，这大到足以生成有趣的模式，同时又足够小，可确保能够快速地模拟随机漫步。然后，我们创建了两个用于存储x 和y 值的列表，并让**每次漫步都从点(0, 0)出发**。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15.3.2 选择方向"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from random import choice\n",
    "\n",
    "class RandomWalk:\n",
    "    def __init__(self, num_points=5000):\n",
    "        self.num_points = num_points\n",
    "        self.x_values = [0]\n",
    "        self.y_values = [0]\n",
    "        \n",
    "    def fill_walk(self):\n",
    "        # 不断漫步，直到列表达到指定的长度\n",
    "        while len(self.x_values) < self.num_points:\n",
    "        # 决定前进方向以及沿这个方向前进的距离\n",
    "            x_direction = choice([1, -1])\n",
    "            x_distance = choice([0, 1, 2, 3, 4]) \n",
    "            x_step = x_direction * x_distance\n",
    "\n",
    "            y_direction = choice([1, -1])\n",
    "            y_distance = choice([0, 1, 2, 3, 4]) \n",
    "            y_step = y_direction * y_distance\n",
    "\n",
    "            # 拒绝原地踏步\n",
    "            if x_step == 0 and y_step == 0:\n",
    "                continue\n",
    "\n",
    "            # 计算下一个点的x和y值\n",
    "            next_x = self.x_values[-1] + x_step\n",
    "            next_y = self.y_values[-1] + y_step\n",
    "            self.x_values.append(next_x)\n",
    "            self.y_values.append(next_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15.3.3 绘制随机漫步图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 创建一个RandomWalk实例，并将其包含的点都绘制出来\n",
    "rw = RandomWalk()\n",
    "rw.fill_walk() \n",
    "plt.scatter(rw.x_values, rw.y_values, s=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15.3.4 模拟多次随机漫步"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "每次随机漫步都不同，因此探索可能生成的各种模式很有趣。要在不多次运行程序的情况下使用前面的代码模拟多次随机漫步，一种办法是将这些代码放在一个while 循环中，\n",
    "如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Make another walk? (y/n): y\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Make another walk? (y/n): n\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 只要程序处于活动状态，就不断地模拟随机漫步\n",
    "while True:\n",
    "    # 创建一个RandomWalk实例，并将其包含的点都绘制出来\n",
    "    rw = RandomWalk()\n",
    "    rw.fill_walk()\n",
    "    plt.scatter(rw.x_values, rw.y_values, s=15)\n",
    "    plt.show()\n",
    "    keep_running = input(\"Make another walk? (y/n): \")\n",
    "    if keep_running == 'n':\n",
    "        break\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15.3.5 设置随机漫步图的样式"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在本节中，我们将定制图表，以**突出每次漫步的重要特征**，并让分散注意力的元素不那么显眼。为此，我们确定要突出的元素，如漫步的起点、终点和经过的路径。接下来确定\n",
    "要使其不那么显眼的元素，如刻度标记和标签。最终的结果是**简单的可视化表示，清楚地指出了每次漫步经过的路径**。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15.3.6 给点着色"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们将使用**颜色映射**来指出漫步中各点的先后顺序，并删除每个点的黑色轮廓，让它们的颜色更明显。为根据漫步中各点的先后顺序进行着色，我们传递参数c ，并将其设置为一个列表，其中包含各点的先后顺序。由于这些点是按顺序绘制的，因此给参数c 指定的列表只需包含数字1~5000，"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Make another walk? (y/n): n\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 只要程序处于活动状态，就不断地模拟随机漫步\n",
    "while True:\n",
    "    # 创建一个RandomWalk实例，并将其包含的点都绘制出来\n",
    "    rw = RandomWalk()\n",
    "    rw.fill_walk()\n",
    "    point_numbers = list(range(rw.num_points))\n",
    "    plt.scatter(rw.x_values, rw.y_values, c=point_numbers, cmap=plt.cm.Blues, edgecolor='none', s=15)\n",
    "    plt.show()\n",
    "    keep_running = input(\"Make another walk? (y/n): \")\n",
    "    if keep_running == 'n':\n",
    "        break\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15.3.7 重新绘制起点和终点"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "除了给随机漫步的各个点着色，以指出它们的先后顺序外，如果还能呈现随机漫步的起点和终点就更好了。为此，可在绘制随机漫步图后重新绘制起点和终点。我们让起点和终\n",
    "点变得更大，并显示为不同的颜色，以突出它们，"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Make another walk? (y/n): n\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 只要程序处于活动状态，就不断地模拟随机漫步\n",
    "while True:\n",
    "    # 创建一个RandomWalk实例，并将其包含的点都绘制出来\n",
    "    rw = RandomWalk()\n",
    "    rw.fill_walk()\n",
    "    point_numbers = list(range(rw.num_points))\n",
    "    plt.scatter(rw.x_values, rw.y_values, c=point_numbers, cmap=plt.cm.Blues, edgecolor='none', s=15)\n",
    "    plt.scatter(0, 0, c='green', edgecolors='none', s=100)\n",
    "    plt.scatter(rw.x_values[-1], rw.y_values[-1], c='red', edgecolors='none', s=100)\n",
    "    plt.show()\n",
    "    keep_running = input(\"Make another walk? (y/n): \")\n",
    "    if keep_running == 'n':\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为突出起点，我们使用绿色绘制点(0, 0)，并使其比其他点大（s=100 ）。为突出终点，我们在漫步包含的最后一个 x 和 y 值处绘制一个点，将其颜色设置为红色，并将尺寸设置\n",
    "为100。请务必将这些代码放在调用plt.show() 的代码前面，确保在其他点的上面绘制起点和终点。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15.3.8 隐藏坐标轴"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-10-ebdf985cf1af>:12: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  plt.axes().get_xaxis().set_visible(False)\n",
      "<ipython-input-10-ebdf985cf1af>:13: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  plt.axes().get_yaxis().set_visible(False)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Make another walk? (y/n): n\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 只要程序处于活动状态，就不断地模拟随机漫步\n",
    "while True:\n",
    "    # 创建一个RandomWalk实例，并将其包含的点都绘制出来\n",
    "    rw = RandomWalk()\n",
    "    rw.fill_walk()\n",
    "    point_numbers = list(range(rw.num_points))\n",
    "    plt.scatter(rw.x_values, rw.y_values, c=point_numbers, cmap=plt.cm.Blues, edgecolor='none', s=15)\n",
    "    plt.scatter(0, 0, c='green', edgecolors='none', s=100)\n",
    "    plt.scatter(rw.x_values[-1], rw.y_values[-1], c='red', edgecolors='none', s=100)\n",
    "    plt.axes().get_xaxis().set_visible(False)\n",
    "    plt.axes().get_yaxis().set_visible(False)\n",
    "    plt.show()\n",
    "    keep_running = input(\"Make another walk? (y/n): \")\n",
    "    if keep_running == 'n':\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15.3.9 增加点数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面来增加点数，以提供更多的数据。为此，我们在创建RandomWalk 实例时增大num_points 的值，并在绘图时调整每个点的大小"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-12-42666a6307ae>:7: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  plt.axes().get_xaxis().set_visible(False)\n",
      "<ipython-input-12-42666a6307ae>:8: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  plt.axes().get_yaxis().set_visible(False)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rw = RandomWalk(50000)\n",
    "rw.fill_walk()\n",
    "point_numbers = list(range(rw.num_points))\n",
    "plt.scatter(rw.x_values, rw.y_values, c=point_numbers, cmap=plt.cm.Blues, edgecolor='none', s=15)\n",
    "plt.scatter(0, 0, c='green', edgecolors='none', s=100)\n",
    "plt.scatter(rw.x_values[-1], rw.y_values[-1], c='red', edgecolors='none', s=100)\n",
    "plt.axes().get_xaxis().set_visible(False)\n",
    "plt.axes().get_yaxis().set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15.3.10 调整尺寸以适合屏幕"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-19-4219954a6efa>:11: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  plt.axes().get_xaxis().set_visible(False)\n",
      "<ipython-input-19-4219954a6efa>:12: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  plt.axes().get_yaxis().set_visible(False)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rw = RandomWalk()\n",
    "rw.fill_walk()\n",
    "\n",
    "# 设置绘图窗口的尺寸\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "point_numbers = list(range(rw.num_points))\n",
    "plt.scatter(rw.x_values, rw.y_values, c=point_numbers, cmap=plt.cm.Blues, edgecolor='none', s=15)\n",
    "plt.scatter(0, 0, c='green', edgecolors='none', s=100)\n",
    "plt.scatter(rw.x_values[-1], rw.y_values[-1], c='red', edgecolors='none', s=100)\n",
    "plt.axes().get_xaxis().set_visible(False)\n",
    "plt.axes().get_yaxis().set_visible(False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "函数figure() 用于指定图表的**宽度、高度、分辨率和背景色**。你需要给形参figsize 指定一个元组，向matplotlib指出绘图窗口的尺寸，单位为英寸。\n",
    "Python假定屏幕分辨率为80像素/英寸，如果上述代码指定的图表尺寸不合适，可根据需要调整其中的数字。如果你知道自己的系统的分辨率，可使用形参dpi 向figure() 传递该分辨率，以有效地利用可用的屏幕空间"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 动手试一试"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**15-3 分子运动** ：修改rw_visual.py，将其中的plt.scatter() 替换为plt.plot() 。为模拟花粉在水滴表面的运动路径，向plt.plot() 传递rw.x_values 和rw.y_values ，并指定实参值linewidth 。使用5000个点而不是50 000个点。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from random import choice\n",
    "\n",
    "class RandomWalk:\n",
    "    def __init__(self, num_points=5000):\n",
    "        self.num_points = num_points\n",
    "        self.x_values = [0]\n",
    "        self.y_values = [0]\n",
    "        \n",
    "    def get_step(self):\n",
    "        direction = choice([1, -1])\n",
    "        distance = choice([0, 1, 2, 3, 4]) \n",
    "        return direction * distance\n",
    "    \n",
    "    def fill_walk(self):\n",
    "        # 不断漫步，直到列表达到指定的长度\n",
    "        while len(self.x_values) < self.num_points:\n",
    "        # 决定前进方向以及沿这个方向前进的距离\n",
    "            x_step = self.get_step() \n",
    "            y_step = self.get_step()\n",
    "            # 拒绝原地踏步\n",
    "            if x_step == 0 and y_step == 0:\n",
    "                continue\n",
    "\n",
    "            # 计算下一个点的x和y值\n",
    "            next_x = self.x_values[-1] + x_step\n",
    "            next_y = self.y_values[-1] + y_step\n",
    "            self.x_values.append(next_x)\n",
    "            self.y_values.append(next_y)\n",
    "    \n",
    "    \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-40-e66f6cda7145>:14: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  plt.axes().get_xaxis().set_visible(False)\n",
      "<ipython-input-40-e66f6cda7145>:15: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  plt.axes().get_yaxis().set_visible(False)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Make another walk? (y/n): n\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 只要程序处于活动状态，就不断地模拟随机漫步\n",
    "while True:\n",
    "    # 创建一个RandomWalk实例，并将其包含的点都绘制出来\n",
    "    rw = RandomWalk()\n",
    "    rw.fill_walk()\n",
    "    # 设置绘图窗口的尺寸\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    point_numbers = list(range(rw.num_points))\n",
    "    plt.plot(rw.x_values, rw.y_values, linewidth=2)\n",
    "    plt.scatter(0, 0, c='green', edgecolors='none', s=100)\n",
    "    plt.scatter(rw.x_values[-1], rw.y_values[-1], c='red', edgecolors='none', s=100)\n",
    "    plt.axes().get_xaxis().set_visible(False)\n",
    "    plt.axes().get_yaxis().set_visible(False)\n",
    "    plt.show()\n",
    "    keep_running = input(\"Make another walk? (y/n): \")\n",
    "    if keep_running == 'n':\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 15.4 使用Pygal模拟掷骰子"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在本节中，我们将使用Python可视化包Pygal来生成可缩放的矢量图形文件。对于需要在尺寸不同的屏幕上显示的图表，这很有用，因为它们将自动缩放，以适合观看者的屏幕。**如果你打算以在线方式使用图表，请考虑使用Pygal来生成它们，这样它们在任何设备上显示时都会很美观。**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15.4.1 安装Pygal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pygal\n",
      "  Downloading pygal-2.4.0-py2.py3-none-any.whl (127 kB)\n",
      "Installing collected packages: pygal\n",
      "Successfully installed pygal-2.4.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -pencv-python (c:\\users\\mc\\pycharmprojects\\pythonproject\\venv\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -pencv-python (c:\\users\\mc\\pycharmprojects\\pythonproject\\venv\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -pencv-python (c:\\users\\mc\\pycharmprojects\\pythonproject\\venv\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -pencv-python (c:\\users\\mc\\pycharmprojects\\pythonproject\\venv\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -pencv-python (c:\\users\\mc\\pycharmprojects\\pythonproject\\venv\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -pencv-python (c:\\users\\mc\\pycharmprojects\\pythonproject\\venv\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -pencv-python (c:\\users\\mc\\pycharmprojects\\pythonproject\\venv\\lib\\site-packages)\n",
      "WARNING: You are using pip version 21.1.2; however, version 21.1.3 is available.\n",
      "You should consider upgrading via the 'c:\\users\\mc\\pycharmprojects\\pythonproject\\venv\\scripts\\python.exe -m pip install --upgrade pip' command.\n"
     ]
    }
   ],
   "source": [
    "!pip install pygal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 15.4.2 Pygal画廊"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "要了解使用Pygal可创建什么样的图表，请查看图表类型画廊：访问http://www.pygal.org/ ，单击Documentation，再单击Chart types。每个示例都包含源代码，让你知道这些图表是如何生成的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15.4.3 创建Die 类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from random import randint\n",
    "\n",
    "class Die():\n",
    "    \"\"\"表示一个骰子的类\"\"\"\n",
    "    \n",
    "    def __init__(self, num_sides=6):\n",
    "        \"\"\"骰子默认为6面\"\"\"\n",
    "        self.num_sides = num_sides\n",
    "        \n",
    "    def roll(self):\n",
    "        \"\"\"\"返回一个位于1和骰子面数之间的随机值\"\"\" \n",
    "        return randint(1, self.num_sides)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 15.4.4 掷骰子"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用这个类来创建图表前，先来掷D6骰子，将结果打印出来，并检查结果是否**合理：**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4, 5, 5, 5, 2, 3, 4, 5, 6, 5, 3, 2, 2, 1, 3, 1, 5, 5, 2, 2, 3, 3, 3, 5, 5, 2, 1, 3, 4, 2, 1, 6, 2, 2, 6, 6, 6, 4, 1, 3, 1, 6, 3, 1, 6, 5, 5, 4, 4, 3]\n"
     ]
    }
   ],
   "source": [
    "# 创建一个D6 \n",
    "die = Die()\n",
    "# 掷几次骰子，并将结果存储在一个列表中\n",
    "results = [] \n",
    "for roll_num in range(1000):\n",
    "    result = die.roll()\n",
    "    results.append(result)\n",
    "print(results[:50])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过快速扫描这些结果可知，Die 类看起来没有问题。我们见到了值1和6，这表明返回了最大和最小的可能值；我们没有见到0或7，这表明结果都在正确的范围内。我们还看到了1~6的所有数字，这表明所有可能的结果都出现了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15.4.5 分析结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[158, 189, 151, 160, 167, 175]\n"
     ]
    }
   ],
   "source": [
    "frequencies = [] \n",
    "for value in range(1, die.num_sides+1): \n",
    "    frequency = results.count(value) \n",
    "    frequencies.append(frequency)\n",
    "print(frequencies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "结果看起来是合理的：我们看到了6个值——掷D6骰子时可能出现的每个点数对应一个；我们还发现，没有任何点数出现的频率比其他点数高很多。下面来可视化这些结果。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15.4.6 绘制直方图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有了频率列表后，我们就可以绘制一个表示结果的直方图。直方图 是一种条形图，指出了各种结果出现的频率。创建这种直方图的代码如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pygal\n",
    "# 对结果进行可视化\n",
    "hist = pygal.Bar()  # 为创建条形图，我们创建了一个pygal.Bar() 实例\n",
    "hist.title = \"Results of rolling one D6 1000 times.\"  \n",
    "hist.x_labels = ['1', '2', '3', '4', '5', '6']\n",
    "hist.x_title = \"Result\"\n",
    "hist.y_title = \"Frequency of Result\"\n",
    "hist.add('D6', frequencies)\n",
    "hist.render_to_file('die_visual.svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "要查看生成的直方图，最简单的方式是使用Web浏览器。为此，在任何Web浏览器中新建一个标签页，再在其中打开文件die_visual.svg（它位于die_visual.py所在的文件夹中）。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15.4.7 同时掷两个骰子"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "同时掷两个骰子时，得到的点数更多，结果分布情况也不同。下面来修改前面的代码，创建两个D6骰子，以模拟同时掷两个骰子的情况。每次掷两个骰子时，我们都将两个骰子\n",
    "的点数相加，并将结果存储在results 中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[27, 57, 94, 98, 149, 162, 138, 113, 74, 63, 25]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pygal\n",
    "\n",
    "die_1 = Die()\n",
    "die_2 = Die()\n",
    "\n",
    "results = []\n",
    "for roll in range(1000):\n",
    "    result = die_1.roll() + die_2.roll()\n",
    "    results.append(result)\n",
    "\n",
    "# 分析结果\n",
    "frequencies = []\n",
    "max_result = die_1.num_sides + die_2.num_sides\n",
    "for value in range(2, max_result+1):\n",
    "    frequency = results.count(value)\n",
    "    frequencies.append(frequency)\n",
    "frequencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 对结果进行可视化\n",
    "hist = pygal.Bar()\n",
    "\n",
    "hist.title = \"Results of rolling two D6 dice 1000 times.\"\n",
    "hist.x_labels = ['2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12']\n",
    "hist.x_title = 'Result'\n",
    "hist.y_title = 'Frequency of Result'\n",
    "\n",
    "hist.add('D6+D6', frequencies)\n",
    "hist.render_to_file('dice_visual.svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15.4.8 同时掷两个面数不同的骰子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pygal\n",
    "\n",
    "die_1 = Die(6)\n",
    "die_2 = Die(10)\n",
    "\n",
    "results = []\n",
    "for roll in range(1000):\n",
    "    result = die_1.roll() + die_2.roll()\n",
    "    results.append(result)\n",
    "\n",
    "# 分析结果\n",
    "frequencies = []\n",
    "max_result = die_1.num_sides + die_2.num_sides\n",
    "for value in range(2, max_result+1):\n",
    "    frequency = results.count(value)\n",
    "    frequencies.append(frequency)\n",
    "    \n",
    "# 对结果进行可视化\n",
    "hist = pygal.Bar()\n",
    "\n",
    "hist.title = \"Results of rolling a D6 and a D10 50,000 times.\"\n",
    "hist.x_labels = ['2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16']\n",
    "hist.x_title = 'Result'\n",
    "hist.y_title = 'Frequency of Result'\n",
    "\n",
    "hist.add('D6+D10', frequencies)\n",
    "hist.render_to_file('dice_visual.svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "显示了最终的图表。可能性最大的点数不是一个，而是5个，这是因为导致出现最小点数和最大点数的组合都只有一种（1和1以及6和10），但面数较小的骰子限制了得到中间点数的组合数：得到总点数7、8、9、10和11的组合数都是6种。因此，这些总点数是最常见的结果，它们出现的可能性相同。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 动手试一试"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**15-6 自动生成标签** ：请修改die.py和dice_visual.py，将用来设置hist.x_labels 值的列表替换为一个自动生成这种列表的循环。如果你熟悉列表解析，可尝试将die_visual.py和dice_visual.py中的其他for 循环也替换为列表解析。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pygal\n",
    "\n",
    "die_1 = Die(6)\n",
    "die_2 = Die(10)\n",
    "\n",
    "def two_dice_roll(die_1, die_2, n=1000):\n",
    "    results = []\n",
    "    for roll in range(n):\n",
    "        result = die_1.roll() + die_2.roll()\n",
    "        results.append(result)\n",
    "\n",
    "    # 分析结果\n",
    "    frequencies = []\n",
    "    max_result = die_1.num_sides + die_2.num_sides\n",
    "    for value in range(2, max_result+1):\n",
    "        frequency = results.count(value)\n",
    "        frequencies.append(frequency)\n",
    "\n",
    "    # 对结果进行可视化\n",
    "    hist = pygal.Bar()\n",
    "\n",
    "    hist.title = f\"Results of rolling a D{die_1.num_sides} and a D{die_2.num_sides} {n} times.\"\n",
    "    hist.x_labels = [str(i) for i in range(2, max_result+1)]\n",
    "    hist.x_title = 'Result'\n",
    "    hist.y_title = 'Frequency of Result'\n",
    "\n",
    "    hist.add(f'D{die_1.num_sides}+D{die_2.num_sides}', frequencies)\n",
    "    hist.render_to_file('dice_visual.svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**15-7 两个D8骰子**： 请模拟同时掷两个8面骰子1000次的结果。逐渐增加掷骰子的次数，直到系统不堪重负为止。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "die_1 = Die(8)\n",
    "die_2 = Die(8)\n",
    "two_dice_roll(die_1, die_2, n=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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